Overview

Dataset statistics

Number of variables22
Number of observations39607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory176.0 B

Variable types

Numeric21
Categorical1

Alerts

X_23 has constant value "1" Constant
X_17 is highly correlated with X_18High correlation
X_18 is highly correlated with X_17High correlation
X_19 is highly correlated with X_20 and 2 other fieldsHigh correlation
X_20 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_22 is highly correlated with X_19 and 2 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 4 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_06 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_07 is highly correlated with Y_04 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_05 and 7 other fieldsHigh correlation
X_17 is highly correlated with X_18High correlation
X_18 is highly correlated with X_17High correlation
X_19 is highly correlated with X_20 and 2 other fieldsHigh correlation
X_20 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_22 is highly correlated with X_19 and 2 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 8 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_10High correlation
Y_07 is highly correlated with Y_04 and 2 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 7 other fieldsHigh correlation
X_17 is highly correlated with X_18High correlation
X_18 is highly correlated with X_17High correlation
X_19 is highly correlated with X_21High correlation
X_20 is highly correlated with X_21 and 1 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 1 other fieldsHigh correlation
X_22 is highly correlated with X_20High correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05High correlation
Y_05 is highly correlated with Y_04High correlation
Y_06 is highly correlated with Y_08 and 6 other fieldsHigh correlation
Y_08 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_09 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_10 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_11 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_12 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_13 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_14 is highly correlated with Y_06 and 6 other fieldsHigh correlation
X_17 is highly correlated with X_18High correlation
X_18 is highly correlated with X_17High correlation
X_19 is highly correlated with X_20 and 2 other fieldsHigh correlation
X_20 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_22 is highly correlated with X_19 and 2 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 4 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_01 and 8 other fieldsHigh correlation
Y_07 is highly correlated with Y_01 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_10 is highly correlated with Y_01 and 10 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 8 other fieldsHigh correlation

Reproduction

Analysis started2022-08-06 10:02:37.337913
Analysis finished2022-08-06 10:03:42.702018
Duration1 minute and 5.36 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

X_17
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.51258868
Minimum13.41
Maximum13.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:42.780783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.41
5-th percentile13.47
Q113.5
median13.51
Q313.53
95-th percentile13.55
Maximum13.61
Range0.2
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.02343602228
Coefficient of variation (CV)0.001734384346
Kurtosis0.2487694617
Mean13.51258868
Median Absolute Deviation (MAD)0.01
Skewness-0.1869845749
Sum535193.1
Variance0.0005492471405
MonotonicityNot monotonic
2022-08-06T19:03:42.900499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
13.528730
22.0%
13.516187
15.6%
13.55398
13.6%
13.534579
11.6%
13.493482
 
8.8%
13.543354
 
8.5%
13.482455
 
6.2%
13.551701
 
4.3%
13.471393
 
3.5%
13.56802
 
2.0%
Other values (11)1526
 
3.9%
ValueCountFrequency (%)
13.413
 
< 0.1%
13.428
 
< 0.1%
13.4333
 
0.1%
13.4489
 
0.2%
13.45254
 
0.6%
13.46699
 
1.8%
13.471393
 
3.5%
13.482455
6.2%
13.493482
8.8%
13.55398
13.6%
ValueCountFrequency (%)
13.611
 
< 0.1%
13.64
 
< 0.1%
13.5919
 
< 0.1%
13.58102
 
0.3%
13.57314
 
0.8%
13.56802
 
2.0%
13.551701
 
4.3%
13.543354
 
8.5%
13.534579
11.6%
13.528730
22.0%

X_18
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.44926326
Minimum13.26
Maximum13.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:43.033493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.26
5-th percentile13.4
Q113.43
median13.45
Q313.47
95-th percentile13.5
Maximum13.57
Range0.31
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.02909346806
Coefficient of variation (CV)0.002163201619
Kurtosis0.6676156493
Mean13.44926326
Median Absolute Deviation (MAD)0.02
Skewness-0.1156391426
Sum532684.97
Variance0.0008464298837
MonotonicityNot monotonic
2022-08-06T19:03:43.159443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13.467516
19.0%
13.455672
14.3%
13.444908
12.4%
13.434131
10.4%
13.473534
8.9%
13.422862
 
7.2%
13.482374
 
6.0%
13.411974
 
5.0%
13.491794
 
4.5%
13.41111
 
2.8%
Other values (21)3731
9.4%
ValueCountFrequency (%)
13.261
 
< 0.1%
13.272
 
< 0.1%
13.281
 
< 0.1%
13.31
 
< 0.1%
13.311
 
< 0.1%
13.322
 
< 0.1%
13.337
 
< 0.1%
13.3411
 
< 0.1%
13.3552
0.1%
13.3698
0.2%
ValueCountFrequency (%)
13.572
 
< 0.1%
13.564
 
< 0.1%
13.5517
 
< 0.1%
13.5448
 
0.1%
13.53183
 
0.5%
13.52349
 
0.9%
13.51615
 
1.6%
13.51057
2.7%
13.491794
4.5%
13.482374
6.0%

X_19
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.240228747
Minimum2.86
Maximum3.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:43.312749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.09
Q13.16
median3.22
Q33.31
95-th percentile3.45
Maximum3.75
Range0.89
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1104820121
Coefficient of variation (CV)0.03409697917
Kurtosis-0.2801965986
Mean3.240228747
Median Absolute Deviation (MAD)0.07
Skewness0.4878063901
Sum128335.74
Variance0.012206275
MonotonicityNot monotonic
2022-08-06T19:03:43.459236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.191618
 
4.1%
3.181575
 
4.0%
3.171547
 
3.9%
3.161514
 
3.8%
3.221447
 
3.7%
3.21414
 
3.6%
3.211404
 
3.5%
3.131398
 
3.5%
3.151377
 
3.5%
3.231332
 
3.4%
Other values (72)24981
63.1%
ValueCountFrequency (%)
2.861
 
< 0.1%
2.91
 
< 0.1%
2.913
 
< 0.1%
2.924
 
< 0.1%
2.936
 
< 0.1%
2.947
 
< 0.1%
2.953
 
< 0.1%
2.969
 
< 0.1%
2.9723
0.1%
2.9822
0.1%
ValueCountFrequency (%)
3.751
 
< 0.1%
3.741
 
< 0.1%
3.721
 
< 0.1%
3.691
 
< 0.1%
3.661
 
< 0.1%
3.651
 
< 0.1%
3.641
 
< 0.1%
3.631
 
< 0.1%
3.621
 
< 0.1%
3.617
< 0.1%

X_20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.184492893
Minimum2.83
Maximum3.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:43.615783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.83
5-th percentile3.03
Q13.1
median3.18
Q33.27
95-th percentile3.35
Maximum3.67
Range0.84
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1052659096
Coefficient of variation (CV)0.03305578412
Kurtosis-0.7374856872
Mean3.184492893
Median Absolute Deviation (MAD)0.08
Skewness0.07494735651
Sum126128.21
Variance0.01108091172
MonotonicityNot monotonic
2022-08-06T19:03:43.767221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.111445
 
3.6%
3.11344
 
3.4%
3.091338
 
3.4%
3.121322
 
3.3%
3.291284
 
3.2%
3.261250
 
3.2%
3.081231
 
3.1%
3.131223
 
3.1%
3.251190
 
3.0%
3.281188
 
3.0%
Other values (67)26792
67.6%
ValueCountFrequency (%)
2.831
 
< 0.1%
2.846
 
< 0.1%
2.859
< 0.1%
2.866
 
< 0.1%
2.876
 
< 0.1%
2.886
 
< 0.1%
2.8914
< 0.1%
2.913
< 0.1%
2.9116
< 0.1%
2.9216
< 0.1%
ValueCountFrequency (%)
3.671
 
< 0.1%
3.621
 
< 0.1%
3.591
 
< 0.1%
3.581
 
< 0.1%
3.571
 
< 0.1%
3.552
< 0.1%
3.542
< 0.1%
3.532
< 0.1%
3.513
< 0.1%
3.54
< 0.1%

X_21
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.174270457
Minimum2.83
Maximum3.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:43.925333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.83
5-th percentile3.03
Q13.09
median3.16
Q33.25
95-th percentile3.37
Maximum3.68
Range0.85
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.1068682246
Coefficient of variation (CV)0.03366701924
Kurtosis-0.2758190559
Mean3.174270457
Median Absolute Deviation (MAD)0.07
Skewness0.5237540345
Sum125723.33
Variance0.01142081742
MonotonicityNot monotonic
2022-08-06T19:03:44.077858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.111735
 
4.4%
3.091733
 
4.4%
3.121714
 
4.3%
3.11681
 
4.2%
3.081606
 
4.1%
3.131498
 
3.8%
3.071409
 
3.6%
3.141383
 
3.5%
3.171351
 
3.4%
3.151333
 
3.4%
Other values (67)24164
61.0%
ValueCountFrequency (%)
2.834
 
< 0.1%
2.841
 
< 0.1%
2.862
 
< 0.1%
2.873
 
< 0.1%
2.888
 
< 0.1%
2.8910
< 0.1%
2.912
< 0.1%
2.9110
< 0.1%
2.9220
0.1%
2.9317
< 0.1%
ValueCountFrequency (%)
3.681
 
< 0.1%
3.612
 
< 0.1%
3.582
 
< 0.1%
3.572
 
< 0.1%
3.563
 
< 0.1%
3.554
 
< 0.1%
3.547
< 0.1%
3.536
< 0.1%
3.525
< 0.1%
3.5111
< 0.1%

X_22
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct83
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.232672507
Minimum2.85
Maximum3.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:44.233988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.85
5-th percentile3.063
Q13.14
median3.23
Q33.32
95-th percentile3.4
Maximum3.79
Range0.94
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1089813432
Coefficient of variation (CV)0.03371246019
Kurtosis-0.6631844791
Mean3.232672507
Median Absolute Deviation (MAD)0.09
Skewness0.04265760619
Sum128036.46
Variance0.01187693317
MonotonicityNot monotonic
2022-08-06T19:03:44.385851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.131288
 
3.3%
3.31274
 
3.2%
3.191244
 
3.1%
3.311232
 
3.1%
3.121229
 
3.1%
3.211195
 
3.0%
3.161180
 
3.0%
3.221177
 
3.0%
3.141168
 
2.9%
3.171164
 
2.9%
Other values (73)27456
69.3%
ValueCountFrequency (%)
2.853
 
< 0.1%
2.862
 
< 0.1%
2.873
 
< 0.1%
2.884
 
< 0.1%
2.896
< 0.1%
2.95
 
< 0.1%
2.919
< 0.1%
2.9210
< 0.1%
2.9312
< 0.1%
2.9413
< 0.1%
ValueCountFrequency (%)
3.791
< 0.1%
3.691
< 0.1%
3.662
< 0.1%
3.641
< 0.1%
3.631
< 0.1%
3.621
< 0.1%
3.612
< 0.1%
3.62
< 0.1%
3.591
< 0.1%
3.582
< 0.1%

X_23
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.6 KiB
1
39607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39607
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
139607
100.0%

Length

2022-08-06T19:03:44.521697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-06T19:03:44.638443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
139607
100.0%

Most occurring characters

ValueCountFrequency (%)
139607
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39607
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
139607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
139607
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139607
100.0%

X_24
Real number (ℝ≥0)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.115673997
Minimum1.83
Maximum2.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:44.737917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.83
5-th percentile2.07
Q12.09
median2.12
Q32.14
95-th percentile2.17
Maximum2.35
Range0.52
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.03244002567
Coefficient of variation (CV)0.0153331873
Kurtosis0.2306472115
Mean2.115673997
Median Absolute Deviation (MAD)0.02
Skewness0.1928660836
Sum83795.5
Variance0.001052355266
MonotonicityNot monotonic
2022-08-06T19:03:44.878924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.074461
11.3%
2.114411
11.1%
2.144264
10.8%
2.124186
10.6%
2.14089
10.3%
2.133717
9.4%
2.153282
8.3%
2.093155
8.0%
2.082256
5.7%
2.162228
5.6%
Other values (26)3558
9.0%
ValueCountFrequency (%)
1.831
 
< 0.1%
2.011
 
< 0.1%
2.025
 
< 0.1%
2.0328
 
0.1%
2.0490
 
0.2%
2.05315
 
0.8%
2.06829
 
2.1%
2.074461
11.3%
2.082256
5.7%
2.093155
8.0%
ValueCountFrequency (%)
2.351
 
< 0.1%
2.343
< 0.1%
2.331
 
< 0.1%
2.321
 
< 0.1%
2.313
< 0.1%
2.33
< 0.1%
2.296
< 0.1%
2.285
< 0.1%
2.273
< 0.1%
2.263
< 0.1%

Y_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2249
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.353813796
Minimum0.017
Maximum4.409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:45.031730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.7833
Q11.1275
median1.349
Q31.576
95-th percentile1.931
Maximum4.409
Range4.392
Interquartile range (IQR)0.4485

Descriptive statistics

Standard deviation0.3562231101
Coefficient of variation (CV)0.2631256316
Kurtosis1.210970899
Mean1.353813796
Median Absolute Deviation (MAD)0.224
Skewness0.1502434869
Sum53620.503
Variance0.1268949041
MonotonicityNot monotonic
2022-08-06T19:03:45.181869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.37664
 
0.2%
1.31262
 
0.2%
1.33360
 
0.2%
1.4260
 
0.2%
1.38960
 
0.2%
1.27860
 
0.2%
1.360
 
0.2%
1.30859
 
0.1%
1.458
 
0.1%
1.26358
 
0.1%
Other values (2239)39006
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.0192
< 0.1%
0.023
< 0.1%
0.0212
< 0.1%
0.0252
< 0.1%
0.0262
< 0.1%
0.0271
 
< 0.1%
0.0281
 
< 0.1%
0.0351
 
< 0.1%
ValueCountFrequency (%)
4.4091
< 0.1%
4.0811
< 0.1%
3.791
< 0.1%
3.721
< 0.1%
3.5291
< 0.1%
3.5181
< 0.1%
3.5011
< 0.1%
3.4991
< 0.1%
3.4191
< 0.1%
3.3641
< 0.1%

Y_02
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2227
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057267251
Minimum0.007
Maximum3.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:45.338851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.45
Q10.793
median1.044
Q31.3
95-th percentile1.711
Maximum3.998
Range3.991
Interquartile range (IQR)0.507

Descriptive statistics

Standard deviation0.386265985
Coefficient of variation (CV)0.3653437527
Kurtosis0.6736418075
Mean1.057267251
Median Absolute Deviation (MAD)0.254
Skewness0.3657652688
Sum41875.184
Variance0.1492014112
MonotonicityNot monotonic
2022-08-06T19:03:45.499364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07259
 
0.1%
0.81459
 
0.1%
1.1258
 
0.1%
1.04357
 
0.1%
0.92456
 
0.1%
0.88856
 
0.1%
1.03155
 
0.1%
1.14455
 
0.1%
0.90255
 
0.1%
0.83454
 
0.1%
Other values (2217)39043
98.6%
ValueCountFrequency (%)
0.0072
 
< 0.1%
0.0084
< 0.1%
0.0093
< 0.1%
0.014
< 0.1%
0.0113
< 0.1%
0.0123
< 0.1%
0.0135
< 0.1%
0.0142
 
< 0.1%
0.0157
< 0.1%
0.0166
< 0.1%
ValueCountFrequency (%)
3.9981
< 0.1%
3.971
< 0.1%
3.721
< 0.1%
3.5521
< 0.1%
3.2881
< 0.1%
3.2561
< 0.1%
3.2281
< 0.1%
3.1421
< 0.1%
3.1151
< 0.1%
3.0491
< 0.1%

Y_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2127
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.014001717
Minimum0.017
Maximum3.756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:45.671149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.451
Q10.769
median0.998
Q31.239
95-th percentile1.628
Maximum3.756
Range3.739
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3614919509
Coefficient of variation (CV)0.3565003341
Kurtosis0.7764764849
Mean1.014001717
Median Absolute Deviation (MAD)0.235
Skewness0.396399124
Sum40161.566
Variance0.1306764305
MonotonicityNot monotonic
2022-08-06T19:03:45.824368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88866
 
0.2%
0.98863
 
0.2%
0.97362
 
0.2%
0.97161
 
0.2%
0.96561
 
0.2%
1.10460
 
0.2%
0.86960
 
0.2%
0.99959
 
0.1%
0.90657
 
0.1%
0.84657
 
0.1%
Other values (2117)39001
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.0214
< 0.1%
0.0221
 
< 0.1%
0.0242
< 0.1%
0.0252
< 0.1%
0.0272
< 0.1%
0.0293
< 0.1%
0.032
< 0.1%
0.0311
 
< 0.1%
ValueCountFrequency (%)
3.7561
< 0.1%
3.7131
< 0.1%
3.2841
< 0.1%
3.2131
< 0.1%
3.1981
< 0.1%
3.1821
< 0.1%
3.1021
< 0.1%
3.0991
< 0.1%
3.0691
< 0.1%
3.0281
< 0.1%

Y_04
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10773
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.62119133
Minimum-0.331
Maximum98.794
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size309.6 KiB
2022-08-06T19:03:45.990156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.331
5-th percentile8.9393
Q111.822
median13.837
Q315.626
95-th percentile17.5587
Maximum98.794
Range99.125
Interquartile range (IQR)3.804

Descriptive statistics

Standard deviation2.686631665
Coefficient of variation (CV)0.1972391107
Kurtosis25.18483477
Mean13.62119133
Median Absolute Deviation (MAD)1.887
Skewness0.4534505598
Sum539494.525
Variance7.217989702
MonotonicityNot monotonic
2022-08-06T19:03:46.143506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.85215
 
< 0.1%
14.24314
 
< 0.1%
13.34914
 
< 0.1%
15.70713
 
< 0.1%
15.46113
 
< 0.1%
13.8913
 
< 0.1%
14.40913
 
< 0.1%
14.77413
 
< 0.1%
15.06313
 
< 0.1%
15.43613
 
< 0.1%
Other values (10763)39473
99.7%
ValueCountFrequency (%)
-0.3311
< 0.1%
-0.3271
< 0.1%
-0.3081
< 0.1%
2.2421
< 0.1%
3.3121
< 0.1%
3.4471
< 0.1%
3.4781
< 0.1%
3.8331
< 0.1%
3.8471
< 0.1%
3.9241
< 0.1%
ValueCountFrequency (%)
98.7941
< 0.1%
33.3331
< 0.1%
25.9561
< 0.1%
21.4621
< 0.1%
21.4421
< 0.1%
20.891
< 0.1%
20.4761
< 0.1%
20.3211
< 0.1%
20.2091
< 0.1%
20.2041
< 0.1%

Y_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10241
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.29046706
Minimum18.589
Maximum37.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:46.297673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.589
5-th percentile26.512
Q129.768
median31.71
Q333.184
95-th percentile34.709
Maximum37.25
Range18.661
Interquartile range (IQR)3.416

Descriptive statistics

Standard deviation2.543221628
Coefficient of variation (CV)0.08127784168
Kurtosis0.489914823
Mean31.29046706
Median Absolute Deviation (MAD)1.658
Skewness-0.7720326285
Sum1239321.529
Variance6.467976249
MonotonicityNot monotonic
2022-08-06T19:03:46.449534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.69218
 
< 0.1%
33.46517
 
< 0.1%
32.49116
 
< 0.1%
31.94915
 
< 0.1%
33.21515
 
< 0.1%
31.71315
 
< 0.1%
32.65915
 
< 0.1%
32.59315
 
< 0.1%
32.715
 
< 0.1%
32.79615
 
< 0.1%
Other values (10231)39451
99.6%
ValueCountFrequency (%)
18.5891
< 0.1%
19.3951
< 0.1%
19.7041
< 0.1%
20.0621
< 0.1%
20.0672
< 0.1%
20.1231
< 0.1%
20.1891
< 0.1%
20.241
< 0.1%
20.4171
< 0.1%
20.4621
< 0.1%
ValueCountFrequency (%)
37.251
< 0.1%
37.2251
< 0.1%
37.1012
< 0.1%
36.9951
< 0.1%
36.9791
< 0.1%
36.9151
< 0.1%
36.8681
< 0.1%
36.8371
< 0.1%
36.8081
< 0.1%
36.8061
< 0.1%

Y_06
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4269
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.52938208
Minimum-19.963
Maximum18.998
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)0.2%
Memory size309.6 KiB
2022-08-06T19:03:46.615610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19.963
5-th percentile15.177
Q116.146
median16.694
Q317.164
95-th percentile17.758
Maximum18.998
Range38.961
Interquartile range (IQR)1.018

Descriptive statistics

Standard deviation1.89301384
Coefficient of variation (CV)0.1145241747
Kurtosis270.339787
Mean16.52938208
Median Absolute Deviation (MAD)0.501
Skewness-15.02970347
Sum654679.236
Variance3.583501399
MonotonicityNot monotonic
2022-08-06T19:03:46.773098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.78238
 
0.1%
16.87236
 
0.1%
17.09933
 
0.1%
16.85932
 
0.1%
17.13832
 
0.1%
16.96732
 
0.1%
16.7232
 
0.1%
16.84731
 
0.1%
16.7631
 
0.1%
16.47531
 
0.1%
Other values (4259)39279
99.2%
ValueCountFrequency (%)
-19.9631
< 0.1%
-19.6021
< 0.1%
-19.5171
< 0.1%
-19.4721
< 0.1%
-19.4431
< 0.1%
-19.3671
< 0.1%
-19.3511
< 0.1%
-19.2521
< 0.1%
-19.232
< 0.1%
-19.0991
< 0.1%
ValueCountFrequency (%)
18.9981
< 0.1%
18.9921
< 0.1%
18.8881
< 0.1%
18.8571
< 0.1%
18.8241
< 0.1%
18.7861
< 0.1%
18.7531
< 0.1%
18.7281
< 0.1%
18.6921
< 0.1%
18.6851
< 0.1%

Y_07
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2394
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.155054107
Minimum0.502
Maximum5.299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:46.935323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.502
5-th percentile2.53
Q12.863
median3.126
Q33.4335
95-th percentile3.864
Maximum5.299
Range4.797
Interquartile range (IQR)0.5705

Descriptive statistics

Standard deviation0.4189399013
Coefficient of variation (CV)0.1327837454
Kurtosis0.7671083874
Mean3.155054107
Median Absolute Deviation (MAD)0.283
Skewness0.08450194006
Sum124962.228
Variance0.1755106409
MonotonicityNot monotonic
2022-08-06T19:03:47.090485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.00557
 
0.1%
2.9456
 
0.1%
3.13653
 
0.1%
2.98852
 
0.1%
3.12852
 
0.1%
3.03351
 
0.1%
3.27251
 
0.1%
2.97651
 
0.1%
3.0351
 
0.1%
3.0850
 
0.1%
Other values (2384)39083
98.7%
ValueCountFrequency (%)
0.5021
< 0.1%
0.6851
< 0.1%
0.7231
< 0.1%
0.8181
< 0.1%
0.8791
< 0.1%
0.9111
< 0.1%
0.9211
< 0.1%
0.9331
< 0.1%
0.9451
< 0.1%
0.9531
< 0.1%
ValueCountFrequency (%)
5.2991
< 0.1%
5.1181
< 0.1%
4.9991
< 0.1%
4.9911
< 0.1%
4.9821
< 0.1%
4.9271
< 0.1%
4.9181
< 0.1%
4.911
< 0.1%
4.8681
< 0.1%
4.851
< 0.1%

Y_08
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3672
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.29483879
Minimum-29.652
Maximum-23.785
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:03:47.245234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.652
5-th percentile-27.4447
Q1-26.689
median-26.254
Q3-25.855
95-th percentile-25.283
Maximum-23.785
Range5.867
Interquartile range (IQR)0.834

Descriptive statistics

Standard deviation0.6605368289
Coefficient of variation (CV)-0.0251203985
Kurtosis0.7493218708
Mean-26.29483879
Median Absolute Deviation (MAD)0.415
Skewness-0.4373902743
Sum-1041459.68
Variance0.4363089024
MonotonicityNot monotonic
2022-08-06T19:03:47.396546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.31441
 
0.1%
-26.09140
 
0.1%
-25.83840
 
0.1%
-26.43139
 
0.1%
-26.43539
 
0.1%
-26.0939
 
0.1%
-26.08138
 
0.1%
-26.37238
 
0.1%
-26.04537
 
0.1%
-26.12236
 
0.1%
Other values (3662)39220
99.0%
ValueCountFrequency (%)
-29.6521
< 0.1%
-29.6421
< 0.1%
-29.6051
< 0.1%
-29.5781
< 0.1%
-29.4521
< 0.1%
-29.3521
< 0.1%
-29.331
< 0.1%
-29.3241
< 0.1%
-29.3092
< 0.1%
-29.3061
< 0.1%
ValueCountFrequency (%)
-23.7851
< 0.1%
-24.0131
< 0.1%
-24.1171
< 0.1%
-24.1421
< 0.1%
-24.1581
< 0.1%
-24.1621
< 0.1%
-24.181
< 0.1%
-24.191
< 0.1%
-24.2071
< 0.1%
-24.2111
< 0.1%

Y_09
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3649
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.30862254
Minimum-29.523
Maximum-23.96
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:03:47.553533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.523
5-th percentile-27.44
Q1-26.702
median-26.266
Q3-25.871
95-th percentile-25.311
Maximum-23.96
Range5.563
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6535798156
Coefficient of variation (CV)-0.02484279877
Kurtosis0.7264309573
Mean-26.30862254
Median Absolute Deviation (MAD)0.414
Skewness-0.4318247115
Sum-1042005.613
Variance0.4271665753
MonotonicityNot monotonic
2022-08-06T19:03:47.699183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.3143
 
0.1%
-26.22841
 
0.1%
-26.2838
 
0.1%
-26.10638
 
0.1%
-26.26537
 
0.1%
-26.12337
 
0.1%
-26.0437
 
0.1%
-26.37537
 
0.1%
-26.1137
 
0.1%
-26.33136
 
0.1%
Other values (3639)39226
99.0%
ValueCountFrequency (%)
-29.5231
< 0.1%
-29.4771
< 0.1%
-29.471
< 0.1%
-29.4271
< 0.1%
-29.3921
< 0.1%
-29.3761
< 0.1%
-29.3511
< 0.1%
-29.3391
< 0.1%
-29.3381
< 0.1%
-29.3311
< 0.1%
ValueCountFrequency (%)
-23.961
< 0.1%
-23.9851
< 0.1%
-24.0911
< 0.1%
-24.1041
< 0.1%
-24.1551
< 0.1%
-24.161
< 0.1%
-24.1891
< 0.1%
-24.2191
< 0.1%
-24.2421
< 0.1%
-24.2761
< 0.1%

Y_10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4458
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.40006244
Minimum-31.119
Maximum-20.052
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:03:47.862111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-31.119
5-th percentile-23.9517
Q1-22.871
median-22.275
Q3-21.791
95-th percentile-21.195
Maximum-20.052
Range11.067
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.920952195
Coefficient of variation (CV)-0.04111382268
Kurtosis10.34745855
Mean-22.40006244
Median Absolute Deviation (MAD)0.529
Skewness-1.837054602
Sum-887199.273
Variance0.8481529455
MonotonicityNot monotonic
2022-08-06T19:03:48.019269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.01433
 
0.1%
-21.99533
 
0.1%
-21.91932
 
0.1%
-22.09632
 
0.1%
-22.32732
 
0.1%
-22.17631
 
0.1%
-22.09231
 
0.1%
-22.32931
 
0.1%
-22.06531
 
0.1%
-21.78630
 
0.1%
Other values (4448)39291
99.2%
ValueCountFrequency (%)
-31.1191
< 0.1%
-30.9491
< 0.1%
-30.9261
< 0.1%
-30.7881
< 0.1%
-30.6191
< 0.1%
-30.5871
< 0.1%
-30.5841
< 0.1%
-30.5481
< 0.1%
-30.5371
< 0.1%
-30.5071
< 0.1%
ValueCountFrequency (%)
-20.0521
 
< 0.1%
-20.0931
 
< 0.1%
-20.131
 
< 0.1%
-20.1471
 
< 0.1%
-20.2241
 
< 0.1%
-20.2351
 
< 0.1%
-20.2721
 
< 0.1%
-20.2883
< 0.1%
-20.311
 
< 0.1%
-20.3311
 
< 0.1%

Y_11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4309
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.32506113
Minimum19.844
Maximum26.703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:03:48.189542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.844
5-th percentile22.815
Q123.836
median24.42
Q324.9115
95-th percentile25.514
Maximum26.703
Range6.859
Interquartile range (IQR)1.0755

Descriptive statistics

Standard deviation0.8301968024
Coefficient of variation (CV)0.03412927919
Kurtosis0.7579205164
Mean24.32506113
Median Absolute Deviation (MAD)0.532
Skewness-0.6749349242
Sum963442.696
Variance0.6892267307
MonotonicityNot monotonic
2022-08-06T19:03:48.343764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.73734
 
0.1%
24.77634
 
0.1%
24.49633
 
0.1%
24.5832
 
0.1%
24.54232
 
0.1%
24.40932
 
0.1%
24.64432
 
0.1%
24.50931
 
0.1%
24.74131
 
0.1%
24.58831
 
0.1%
Other values (4299)39285
99.2%
ValueCountFrequency (%)
19.8441
< 0.1%
20.0311
< 0.1%
20.0451
< 0.1%
20.1011
< 0.1%
20.1751
< 0.1%
20.1941
< 0.1%
20.1991
< 0.1%
20.2951
< 0.1%
20.2981
< 0.1%
20.3341
< 0.1%
ValueCountFrequency (%)
26.7031
< 0.1%
26.6591
< 0.1%
26.6571
< 0.1%
26.5921
< 0.1%
26.5791
< 0.1%
26.5671
< 0.1%
26.5511
< 0.1%
26.5451
< 0.1%
26.4831
< 0.1%
26.481
< 0.1%

Y_12
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3673
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23776173
Minimum-29.544
Maximum-23.722
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:03:48.511088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.544
5-th percentile-27.38
Q1-26.63
median-26.198
Q3-25.799
95-th percentile-25.238
Maximum-23.722
Range5.822
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6563285123
Coefficient of variation (CV)-0.02501465327
Kurtosis0.7459825
Mean-26.23776173
Median Absolute Deviation (MAD)0.413
Skewness-0.4446574078
Sum-1039199.029
Variance0.430767116
MonotonicityNot monotonic
2022-08-06T19:03:48.657621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.02649
 
0.1%
-26.1741
 
0.1%
-26.07640
 
0.1%
-26.35839
 
0.1%
-26.11839
 
0.1%
-26.4438
 
0.1%
-26.29238
 
0.1%
-25.99837
 
0.1%
-25.91937
 
0.1%
-26.35437
 
0.1%
Other values (3663)39212
99.0%
ValueCountFrequency (%)
-29.5441
< 0.1%
-29.4531
< 0.1%
-29.4411
< 0.1%
-29.3671
< 0.1%
-29.3461
< 0.1%
-29.3411
< 0.1%
-29.3351
< 0.1%
-29.311
< 0.1%
-29.2871
< 0.1%
-29.2831
< 0.1%
ValueCountFrequency (%)
-23.7221
< 0.1%
-23.9471
< 0.1%
-23.951
< 0.1%
-24.0671
< 0.1%
-24.1511
< 0.1%
-24.161
< 0.1%
-24.2211
< 0.1%
-24.2281
< 0.1%
-24.2311
< 0.1%
-24.241
< 0.1%

Y_13
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3665
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23386934
Minimum-29.448
Maximum-23.899
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:03:48.813073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.448
5-th percentile-27.366
Q1-26.624
median-26.193
Q3-25.794
95-th percentile-25.2393
Maximum-23.899
Range5.549
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.6550900257
Coefficient of variation (CV)-0.02497115531
Kurtosis0.7518019689
Mean-26.23386934
Median Absolute Deviation (MAD)0.413
Skewness-0.4398630698
Sum-1039044.863
Variance0.4291429417
MonotonicityNot monotonic
2022-08-06T19:03:48.961050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.09742
 
0.1%
-26.18741
 
0.1%
-26.33641
 
0.1%
-26.2840
 
0.1%
-26.15139
 
0.1%
-26.34638
 
0.1%
-26.03938
 
0.1%
-25.96837
 
0.1%
-26.21336
 
0.1%
-25.99536
 
0.1%
Other values (3655)39219
99.0%
ValueCountFrequency (%)
-29.4481
< 0.1%
-29.4431
< 0.1%
-29.3751
< 0.1%
-29.3681
< 0.1%
-29.3551
< 0.1%
-29.351
< 0.1%
-29.3011
< 0.1%
-29.2921
< 0.1%
-29.2361
< 0.1%
-29.2261
< 0.1%
ValueCountFrequency (%)
-23.8991
< 0.1%
-23.9361
< 0.1%
-23.9651
< 0.1%
-24.0211
< 0.1%
-24.1171
< 0.1%
-24.1231
< 0.1%
-24.1771
< 0.1%
-24.1941
< 0.1%
-24.2051
< 0.1%
-24.211
< 0.1%

Y_14
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3682
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.24586843
Minimum-29.62
Maximum-23.856
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:03:49.117970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.62
5-th percentile-27.3817
Q1-26.64
median-26.204
Q3-25.809
95-th percentile-25.245
Maximum-23.856
Range5.764
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6559887312
Coefficient of variation (CV)-0.02499398078
Kurtosis0.734812393
Mean-26.24586843
Median Absolute Deviation (MAD)0.413
Skewness-0.4307872388
Sum-1039520.111
Variance0.4303212155
MonotonicityNot monotonic
2022-08-06T19:03:49.264317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.30346
 
0.1%
-26.02541
 
0.1%
-26.14839
 
0.1%
-25.85839
 
0.1%
-26.0839
 
0.1%
-26.17438
 
0.1%
-26.10538
 
0.1%
-25.83538
 
0.1%
-26.42437
 
0.1%
-26.06537
 
0.1%
Other values (3672)39215
99.0%
ValueCountFrequency (%)
-29.621
< 0.1%
-29.5291
< 0.1%
-29.4931
< 0.1%
-29.4341
< 0.1%
-29.341
< 0.1%
-29.3351
< 0.1%
-29.3121
< 0.1%
-29.2921
< 0.1%
-29.2821
< 0.1%
-29.281
< 0.1%
ValueCountFrequency (%)
-23.8561
< 0.1%
-24.0521
< 0.1%
-24.1372
< 0.1%
-24.1391
< 0.1%
-24.1651
< 0.1%
-24.1761
< 0.1%
-24.1921
< 0.1%
-24.1931
< 0.1%
-24.2081
< 0.1%
-24.2111
< 0.1%

Interactions

2022-08-06T19:03:39.169256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:41.171596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.275267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.178544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.789904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.487537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.186053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.867285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.581163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.273431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.514183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.370286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:13.124410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.987827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.858468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.936412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.696794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.491794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.283331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:33.134195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.918189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:39.313900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:41.326894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.429954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.318220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.929798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.627949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.326146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:58.010334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.724319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.424752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.667966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.509920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:13.273044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:16.138425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:19.000065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:22.078611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.841383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.642390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.431348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:33.283910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:36.062325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:39.450482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:41.470720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.575104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.447741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:50.064031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.759685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.457641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:58.142675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.855018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.565381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.807796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.643768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:13.412638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:16.279049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:19.130934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:22.213896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.977058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.799163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.570998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:33.422993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:36.198817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:39.571184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:41.602369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.701268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.565166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:50.182144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.876779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.575030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:58.261795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.972704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.694038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.933453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.765023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:13.538334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:16.407149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:19.248036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:22.337122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:25.101752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.925824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.696850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:33.547022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:36.320470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:39.696856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:41.739002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.836136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.684176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:50.304775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.998068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.696774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:58.385652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:01.093381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.827650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:08.063512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.892682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:13.668980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:16.536803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:19.367691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:22.463382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:25.228026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:28.056475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.826363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:33.674300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:36.446992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:39.823509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:41.891636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.969317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.803534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:50.428177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:53.119930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.818335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:58.510001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-06T19:03:37.772295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:41.123387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:43.412351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:46.355228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.049350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:51.716375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:54.404349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.101669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:59.797654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:02.511716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:06.685039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:09.560493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:12.351024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.168371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.033249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.175130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:23.909729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:26.694038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:29.500818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:32.318683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.115638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:37.900951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:41.251815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:43.553494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:46.491828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.170428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:51.844748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:54.530922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.227071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:59.925716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:02.636383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:06.822024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:09.696412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:12.478176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.304403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.170721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.301653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.039357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:26.825930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:29.629900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:32.457263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.244718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:38.029752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:41.390417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:43.693234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:46.627259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.292080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:51.971303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:54.656366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.354626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.052212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:02.761233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:06.959632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:09.828735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:12.605809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.440262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.306359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.427059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.169011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:26.957278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:29.759486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:32.592046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.374151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:38.157216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:41.527242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:43.842688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:46.772153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.421335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.104593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:54.788538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.487507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.185682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:02.894875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.102278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:09.969334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:12.740450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.582911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.445985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.558755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.307641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.098899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:29.895779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:32.732391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.507515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:38.779559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:41.659317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:43.984664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:46.908225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.542641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.233028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:54.922365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.613389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.313094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.024090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.238910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.103974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:12.868109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.718548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.580625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.684093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.437494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.231574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.024456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:32.865740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.640079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:38.908954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:41.788942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:44.127929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:47.044756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:49.667096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:52.359405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:55.057210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:02:57.739310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:00.450154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:03.148756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:07.376540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:10.237643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:12.996751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:15.854185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:18.717849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:21.809694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:24.567141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:27.362140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:30.153447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:32.999959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:35.771343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:03:39.038605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-06T19:03:49.423855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-06T19:03:49.676347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-06T19:03:49.927708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-06T19:03:50.177700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-06T19:03:42.012376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-06T19:03:42.485600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X_17X_18X_19X_20X_21X_22X_23X_24Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
013.5213.443.113.173.063.1312.052.0561.4561.68010.50229.63216.0834.276-25.381-25.529-22.76923.792-25.470-25.409-25.304
113.5113.422.973.112.913.2012.101.4461.1841.26818.50733.17916.7363.229-26.619-26.523-22.57424.691-26.253-26.497-26.438
213.5113.433.043.043.013.1212.071.2510.6650.78214.08231.80117.0802.839-26.238-26.216-22.16924.649-26.285-26.215-26.370
313.5113.403.053.013.023.0812.061.4641.0791.05216.97534.50317.1433.144-25.426-25.079-21.76524.913-25.254-25.021-25.345
413.5013.423.043.073.003.1212.090.9830.6460.68915.04732.60217.5693.138-25.376-25.242-21.07225.299-25.072-25.195-24.974
513.5113.443.223.203.163.2212.111.1550.6780.58011.76029.66216.2013.343-26.466-26.527-22.62124.064-26.489-26.536-26.426
613.5013.443.243.113.203.2012.072.1401.4381.68914.13732.73915.5732.418-27.581-28.038-23.35523.051-27.650-27.709-27.599
713.5013.433.253.083.203.1812.121.7691.5351.53417.95434.68018.2303.147-24.917-24.832-20.68926.138-24.539-24.538-24.668
813.5013.453.123.183.133.1112.101.3260.9450.88313.95229.12916.6083.931-25.890-25.801-22.52124.353-25.738-25.825-25.764
913.5113.463.003.093.033.0812.052.0041.7871.54816.88534.20918.1202.646-25.520-25.408-21.15925.961-25.353-25.567-25.470

Last rows

X_17X_18X_19X_20X_21X_22X_23X_24Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
3959713.5013.453.133.063.073.0112.061.4891.3691.30315.68734.08917.5863.107-25.927-25.836-21.61125.399-25.850-25.867-25.587
3959813.4913.443.173.003.093.0812.101.2990.6121.03217.95732.87016.8043.140-26.569-26.304-23.10224.660-26.259-26.410-26.365
3959913.5313.503.163.103.073.1212.100.9490.8910.76717.70630.87717.0902.547-26.652-26.807-22.18824.737-26.783-26.694-26.771
3960013.5213.453.162.993.083.0612.070.9980.5630.9118.87928.95716.4413.387-26.545-26.572-22.70524.084-26.618-26.677-26.530
3960113.4713.413.113.083.093.0312.101.5561.4181.32812.59832.67116.9492.996-26.106-26.281-22.35924.661-26.134-26.300-26.306
3960213.5213.463.203.033.063.1312.101.3821.2151.26310.87429.19416.5823.410-26.486-26.581-22.77224.261-26.491-26.584-26.580
3960313.4913.443.153.063.053.0612.121.4820.6061.0838.75929.85915.6593.406-27.308-27.203-24.67423.427-27.250-27.334-27.325
3960413.5213.463.233.093.073.1212.131.1171.1540.99313.15924.72016.8233.215-26.502-26.687-22.57724.301-26.388-26.425-26.601
3960513.5213.463.183.013.153.0912.070.8950.1870.4779.12326.41215.7574.216-26.760-26.634-24.06623.305-26.536-26.751-26.635
3960613.5213.473.113.053.033.1012.131.1470.3480.85210.42130.74516.7813.307-26.054-26.251-23.25724.450-26.224-26.256-26.093